Overview

Dataset statistics

Number of variables11
Number of observations3437
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory295.5 KiB
Average record size in memory88.0 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with wine_typeHigh correlation
volatile acidity is highly overall correlated with wine_typeHigh correlation
sulphates is highly overall correlated with wine_typeHigh correlation
alcohol is highly overall correlated with residual_sugar_density_meanHigh correlation
residual_sugar_density_mean is highly overall correlated with alcoholHigh correlation
sulfur_dioxide_mean is highly overall correlated with wine_typeHigh correlation
wine_type is highly overall correlated with fixed acidity and 3 other fieldsHigh correlation
chlorides_density_ratio is highly skewed (γ1 = 23.69679707)Skewed

Reproduction

Analysis started2023-10-30 12:11:58.277126
Analysis finished2023-10-30 12:12:23.952696
Duration25.68 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.6616612 × 10-16
Minimum-3.8935487
Maximum2.2224172
Zeros0
Zeros (%)0.0%
Negative1839
Negative (%)53.5%
Memory size27.0 KiB
2023-10-30T12:12:24.168480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-3.8935487
5-th percentile-1.5187884
Q1-0.66561223
median-0.08424059
Q30.541277
95-th percentile2.2224172
Maximum2.2224172
Range6.1159659
Interquartile range (IQR)1.2068892

Descriptive statistics

Standard deviation1.0001455
Coefficient of variation (CV)-1.5013455 × 1015
Kurtosis0.06792651
Mean-6.6616612 × 10-16
Median Absolute Deviation (MAD)0.58137164
Skewness0.39148613
Sum-2.3238078 × 10-12
Variance1.000291
MonotonicityNot monotonic
2023-10-30T12:12:24.534367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2730395182 186
 
5.4%
-0.4667428589 186
 
5.4%
2.222417236 179
 
5.2%
-0.6656122264 156
 
4.5%
-0.1780425733 144
 
4.2%
-0.369262264 142
 
4.1%
-0.08424059027 138
 
4.0%
0.008396120601 136
 
4.0%
0.09989615602 134
 
3.9%
0.1902870728 124
 
3.6%
Other values (48) 1912
55.6%
ValueCountFrequency (%)
-3.893548655 1
 
< 0.1%
-3.296657431 1
 
< 0.1%
-3.015221792 2
 
0.1%
-2.878389342 1
 
< 0.1%
-2.612033909 4
 
0.1%
-2.482340912 5
 
0.1%
-2.354864999 2
 
0.1%
-2.22953164 17
0.5%
-2.106270002 16
0.5%
-1.985012707 19
0.6%
ValueCountFrequency (%)
2.222417236 179
5.2%
2.15368717 14
 
0.4%
2.084317746 10
 
0.3%
2.014296956 15
 
0.4%
1.943612453 12
 
0.3%
1.872251532 16
 
0.5%
1.800201123 13
 
0.4%
1.727447772 23
 
0.7%
1.653977627 23
 
0.7%
1.579776425 26
 
0.8%

volatile acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.201638 × 10-16
Minimum-1.9657601
Maximum2.1909401
Zeros0
Zeros (%)0.0%
Negative2111
Negative (%)61.4%
Memory size27.0 KiB
2023-10-30T12:12:24.895216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1.9657601
5-th percentile-1.2934842
Q1-0.7422386
median-0.22151271
Q30.54264477
95-th percentile2.1909401
Maximum2.1909401
Range4.1567002
Interquartile range (IQR)1.2848834

Descriptive statistics

Standard deviation1.0001455
Coefficient of variation (CV)8.3231851 × 1015
Kurtosis-0.32637848
Mean1.201638 × 10-16
Median Absolute Deviation (MAD)0.59752516
Skewness0.73570764
Sum2.9043434 × 10-13
Variance1.000291
MonotonicityNot monotonic
2023-10-30T12:12:25.233919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.190940127 174
 
5.1%
-0.3673739083 158
 
4.6%
-0.6660611835 146
 
4.2%
-0.5155322292 141
 
4.1%
-0.4411614155 130
 
3.8%
-0.2215127085 120
 
3.5%
-0.8190378701 119
 
3.5%
-0.07787848433 117
 
3.4%
-0.7422385951 109
 
3.2%
-0.5904956452 108
 
3.1%
Other values (100) 2115
61.5%
ValueCountFrequency (%)
-1.96576008 2
 
0.1%
-1.793133972 3
 
0.1%
-1.750467872 3
 
0.1%
-1.707994396 8
 
0.2%
-1.665711813 2
 
0.1%
-1.623618414 16
0.5%
-1.581712514 1
 
< 0.1%
-1.53999245 22
0.6%
-1.498456582 1
 
< 0.1%
-1.457103289 38
1.1%
ValueCountFrequency (%)
2.190940127 174
5.1%
2.134773638 18
 
0.5%
2.106564145 1
 
< 0.1%
2.078269811 19
 
0.6%
2.049890125 4
 
0.1%
2.021424569 5
 
0.1%
1.992872623 9
 
0.3%
1.964233761 17
 
0.5%
1.935507452 6
 
0.2%
1.90669316 18
 
0.5%

citric acid
Real number (ℝ)

Distinct85
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0829849 × 10-17
Minimum-2.1764352
Maximum4.6235647
Zeros0
Zeros (%)0.0%
Negative1905
Negative (%)55.4%
Memory size27.0 KiB
2023-10-30T12:12:25.556687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.1764352
5-th percentile-1.9044352
Q1-0.47643526
median-0.068435263
Q30.54356473
95-th percentile1.6315647
Maximum4.6235647
Range6.7999999
Interquartile range (IQR)1.02

Descriptive statistics

Standard deviation1.0001455
Coefficient of variation (CV)2.449545 × 1016
Kurtosis1.1797706
Mean4.0829849 × 10-17
Median Absolute Deviation (MAD)0.476
Skewness0.32810325
Sum1.9717561 × 10-13
Variance1.000291
MonotonicityNot monotonic
2023-10-30T12:12:25.892516image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1364352623 167
 
4.9%
-0.2724352612 157
 
4.6%
1.155564728 154
 
4.5%
-0.0004352633077 152
 
4.4%
0.1355647356 147
 
4.3%
-0.2044352617 134
 
3.9%
-0.4084352602 125
 
3.6%
-0.06843526278 120
 
3.5%
0.06756473617 110
 
3.2%
-0.5444352591 109
 
3.2%
Other values (75) 2062
60.0%
ValueCountFrequency (%)
-2.176435247 86
2.5%
-2.108435247 16
 
0.5%
-2.040435248 35
1.0%
-1.972435248 16
 
0.5%
-1.904435249 21
 
0.6%
-1.836435249 15
 
0.4%
-1.76843525 17
 
0.5%
-1.70043525 23
 
0.7%
-1.632435251 18
 
0.5%
-1.564435251 25
 
0.7%
ValueCountFrequency (%)
4.623564701 5
0.1%
4.011564706 1
 
< 0.1%
3.671564708 1
 
< 0.1%
3.399564711 1
 
< 0.1%
3.331564711 2
 
0.1%
3.263564712 1
 
< 0.1%
3.195564712 3
0.1%
3.127564713 1
 
< 0.1%
2.991564714 2
 
0.1%
2.923564714 1
 
< 0.1%

pH
Real number (ℝ)

Distinct103
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2513832 × 10-15
Minimum-3.006208
Maximum4.9095722
Zeros0
Zeros (%)0.0%
Negative1838
Negative (%)53.5%
Memory size27.0 KiB
2023-10-30T12:12:26.230374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-3.006208
5-th percentile-1.5726415
Q1-0.70003585
median-0.076746068
Q30.60887269
95-th percentile1.7307943
Maximum4.9095722
Range7.9157802
Interquartile range (IQR)1.3089085

Descriptive statistics

Standard deviation1.0001455
Coefficient of variation (CV)-7.9923202 × 1014
Kurtosis0.35909191
Mean-1.2513832 × 10-15
Median Absolute Deviation (MAD)0.68561876
Skewness0.37780878
Sum-4.3294257 × 10-12
Variance1.000291
MonotonicityNot monotonic
2023-10-30T12:12:26.579793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0144170898 99
 
2.9%
-0.3883909587 97
 
2.8%
-0.1390750461 95
 
2.8%
-0.2014040243 95
 
2.8%
-0.4507199369 91
 
2.6%
0.1102408665 90
 
2.6%
-0.513048915 90
 
2.6%
0.2348988228 88
 
2.6%
-0.887022784 86
 
2.5%
-0.3260619806 85
 
2.5%
Other values (93) 2521
73.3%
ValueCountFrequency (%)
-3.006208041 2
 
0.1%
-2.69456315 1
 
< 0.1%
-2.632234172 2
 
0.1%
-2.507576216 1
 
< 0.1%
-2.445247238 2
 
0.1%
-2.38291826 1
 
< 0.1%
-2.320589282 5
0.1%
-2.258260303 3
0.1%
-2.195931325 6
0.2%
-2.133602347 5
0.1%
ValueCountFrequency (%)
4.909572184 1
 
< 0.1%
4.223953425 1
 
< 0.1%
3.912308534 1
 
< 0.1%
3.725321599 1
 
< 0.1%
3.662992621 1
 
< 0.1%
3.600663643 1
 
< 0.1%
3.476005687 1
 
< 0.1%
3.351347731 2
0.1%
3.289018752 3
0.1%
3.226689774 1
 
< 0.1%

sulphates
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2951781 × 10-17
Minimum-2.6850155
Maximum2.0946268
Zeros0
Zeros (%)0.0%
Negative1856
Negative (%)54.0%
Memory size27.0 KiB
2023-10-30T12:12:26.979638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.6850155
5-th percentile-1.4581813
Q1-0.76063999
median-0.1010794
Q30.60038917
95-th percentile2.0946268
Maximum2.0946268
Range4.7796423
Interquartile range (IQR)1.3610292

Descriptive statistics

Standard deviation1.0001455
Coefficient of variation (CV)1.205695 × 1016
Kurtosis-0.46488821
Mean8.2951781 × 10-17
Median Absolute Deviation (MAD)0.65956059
Skewness0.33361461
Sum2.8155256 × 10-13
Variance1.000291
MonotonicityNot monotonic
2023-10-30T12:12:27.355146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.094626794 173
 
5.0%
-0.181587565 134
 
3.9%
0.137284367 125
 
3.6%
-0.5090786929 123
 
3.6%
-0.3442267063 111
 
3.2%
-1.191875335 110
 
3.2%
-0.6762045715 109
 
3.2%
0.05834966373 105
 
3.1%
-0.2626342491 105
 
3.1%
-0.4263723369 102
 
3.0%
Other values (49) 2240
65.2%
ValueCountFrequency (%)
-2.685015502 1
 
< 0.1%
-2.586105287 1
 
< 0.1%
-2.390674693 3
 
0.1%
-2.294128891 2
 
0.1%
-2.198346304 5
 
0.1%
-2.103314962 5
 
0.1%
-2.00902317 10
 
0.3%
-1.915459507 19
0.6%
-1.822612814 17
0.5%
-1.730472187 27
0.8%
ValueCountFrequency (%)
2.094626794 173
5.0%
2.027499564 12
 
0.3%
1.959998363 20
 
0.6%
1.892119003 21
 
0.6%
1.823857222 19
 
0.6%
1.755208686 23
 
0.7%
1.686168989 22
 
0.6%
1.616733647 27
 
0.8%
1.546898098 15
 
0.4%
1.476657704 26
 
0.8%

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.1440435 × 10-15
Minimum-2.4389778
Maximum1.8202419
Zeros0
Zeros (%)0.0%
Negative1840
Negative (%)53.5%
Memory size27.0 KiB
2023-10-30T12:12:27.832730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.4389778
5-th percentile-1.3709524
Q1-0.87637301
median-0.13204861
Q30.7275225
95-th percentile1.8202419
Maximum1.8202419
Range4.2592196
Interquartile range (IQR)1.6038955

Descriptive statistics

Standard deviation1.0001455
Coefficient of variation (CV)-4.6647631 × 1014
Kurtosis-0.99849697
Mean-2.1440435 × 10-15
Median Absolute Deviation (MAD)0.77682073
Skewness0.28527384
Sum-7.3081263 × 10-12
Variance1.000291
MonotonicityNot monotonic
2023-10-30T12:12:28.176718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.820241864 206
 
6.0%
-0.8763730051 185
 
5.4%
-0.9733772456 176
 
5.1%
-1.170215776 141
 
4.1%
-0.4048058508 137
 
4.0%
-0.5908086066 121
 
3.5%
0.0457956998 121
 
3.5%
-0.2221546791 119
 
3.5%
0.4772167986 113
 
3.3%
-1.07131874 105
 
3.1%
Other values (63) 2013
58.6%
ValueCountFrequency (%)
-2.438977755 2
 
0.1%
-1.998175013 2
 
0.1%
-1.890905591 7
 
0.2%
-1.784759429 11
 
0.3%
-1.679713248 39
 
1.1%
-1.575744483 48
 
1.4%
-1.472831256 38
 
1.1%
-1.37095235 99
2.9%
-1.270087182 82
2.4%
-1.170215776 141
4.1%
ValueCountFrequency (%)
1.820241864 206
6.0%
1.745978779 39
 
1.1%
1.671167621 51
 
1.5%
1.595800243 43
 
1.3%
1.519868309 41
 
1.2%
1.481687978 1
 
< 0.1%
1.443363298 59
 
1.7%
1.404893173 2
 
0.1%
1.366276495 36
 
1.0%
1.340450071 1
 
< 0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8070992
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 KiB
2023-10-30T12:12:28.484134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88268606
Coefficient of variation (CV)0.1520012
Kurtosis0.23914378
Mean5.8070992
Median Absolute Deviation (MAD)1
Skewness0.2104099
Sum19959
Variance0.77913469
MonotonicityNot monotonic
2023-10-30T12:12:28.816081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1483
43.1%
5 1141
33.2%
7 561
 
16.3%
4 129
 
3.8%
8 104
 
3.0%
3 15
 
0.4%
9 4
 
0.1%
ValueCountFrequency (%)
3 15
 
0.4%
4 129
 
3.8%
5 1141
33.2%
6 1483
43.1%
7 561
 
16.3%
8 104
 
3.0%
9 4
 
0.1%
ValueCountFrequency (%)
9 4
 
0.1%
8 104
 
3.0%
7 561
 
16.3%
6 1483
43.1%
5 1141
33.2%
4 129
 
3.8%
3 15
 
0.4%

wine_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
white
2557 
red
880 

Length

Max length5
Median length5
Mean length4.4879255
Min length3

Characters and Unicode

Total characters15425
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowred
3rd rowred
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 2557
74.4%
red 880
 
25.6%

Length

2023-10-30T12:12:29.131513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-30T12:12:29.389629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
white 2557
74.4%
red 880
 
25.6%

Most occurring characters

ValueCountFrequency (%)
e 3437
22.3%
w 2557
16.6%
h 2557
16.6%
i 2557
16.6%
t 2557
16.6%
r 880
 
5.7%
d 880
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15425
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3437
22.3%
w 2557
16.6%
h 2557
16.6%
i 2557
16.6%
t 2557
16.6%
r 880
 
5.7%
d 880
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 15425
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3437
22.3%
w 2557
16.6%
h 2557
16.6%
i 2557
16.6%
t 2557
16.6%
r 880
 
5.7%
d 880
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3437
22.3%
w 2557
16.6%
h 2557
16.6%
i 2557
16.6%
t 2557
16.6%
r 880
 
5.7%
d 880
 
5.7%

residual_sugar_density_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct3046
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8845818 × 10-14
Minimum-1.7251399
Maximum3.7693438
Zeros0
Zeros (%)0.0%
Negative1881
Negative (%)54.7%
Memory size27.0 KiB
2023-10-30T12:12:29.642940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1.7251399
5-th percentile-1.179866
Q1-0.68976624
median-0.074626712
Q30.47200483
95-th percentile1.7024971
Maximum3.7693438
Range5.4944837
Interquartile range (IQR)1.1617711

Descriptive statistics

Standard deviation0.86753401
Coefficient of variation (CV)4.6033238 × 1013
Kurtosis0.1499802
Mean1.8845818 × 10-14
Median Absolute Deviation (MAD)0.58683326
Skewness0.70792436
Sum6.478007 × 10-11
Variance0.75261526
MonotonicityNot monotonic
2023-10-30T12:12:30.000288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7754012227 7
 
0.2%
-0.6785869675 6
 
0.2%
0.005060546335 5
 
0.1%
-0.8960063869 5
 
0.1%
-0.8274983986 5
 
0.1%
-0.599615894 4
 
0.1%
-0.7351995013 4
 
0.1%
-0.9019541141 4
 
0.1%
-0.7135571463 4
 
0.1%
0.09592707432 4
 
0.1%
Other values (3036) 3389
98.6%
ValueCountFrequency (%)
-1.725139932 1
< 0.1%
-1.69926255 1
< 0.1%
-1.668527399 1
< 0.1%
-1.660773528 1
< 0.1%
-1.624900278 1
< 0.1%
-1.583421711 1
< 0.1%
-1.56044043 1
< 0.1%
-1.557108474 1
< 0.1%
-1.529798722 1
< 0.1%
-1.525657138 1
< 0.1%
ValueCountFrequency (%)
3.769343803 1
< 0.1%
2.982606931 1
< 0.1%
2.940209611 1
< 0.1%
2.754568257 1
< 0.1%
2.717184875 1
< 0.1%
2.714865116 1
< 0.1%
2.639942183 1
< 0.1%
2.603695159 1
< 0.1%
2.595225103 1
< 0.1%
2.591690579 1
< 0.1%

chlorides_density_ratio
Real number (ℝ)

SKEWED 

Distinct2975
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0446441
Minimum-590.3975
Maximum1594.8865
Zeros0
Zeros (%)0.0%
Negative964
Negative (%)28.0%
Memory size27.0 KiB
2023-10-30T12:12:30.340625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-590.3975
5-th percentile-3.4109985
Q1-0.11214517
median0.63140498
Q31.3392774
95-th percentile4.7351266
Maximum1594.8865
Range2185.284
Interquartile range (IQR)1.4514226

Descriptive statistics

Standard deviation42.068495
Coefficient of variation (CV)40.270649
Kurtosis904.45242
Mean1.0446441
Median Absolute Deviation (MAD)0.72313604
Skewness23.696797
Sum3590.4417
Variance1769.7583
MonotonicityNot monotonic
2023-10-30T12:12:30.639359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.109285264 8
 
0.2%
7.952576601 5
 
0.1%
0.1698172687 5
 
0.1%
1.801591369 5
 
0.1%
0.4920902965 5
 
0.1%
-17.3205545 4
 
0.1%
13.683016 4
 
0.1%
0.915180671 4
 
0.1%
0.9636965054 4
 
0.1%
0.6231378858 4
 
0.1%
Other values (2965) 3389
98.6%
ValueCountFrequency (%)
-590.3975041 1
< 0.1%
-427.215742 1
< 0.1%
-394.6735481 1
< 0.1%
-362.1625998 1
< 0.1%
-308.1201552 1
< 0.1%
-232.4300652 2
0.1%
-167.7497206 2
0.1%
-137.9401471 1
< 0.1%
-123.0424327 1
< 0.1%
-118.6269497 1
< 0.1%
ValueCountFrequency (%)
1594.886509 1
< 0.1%
1317.721386 1
< 0.1%
440.6286806 1
< 0.1%
377.1011758 1
< 0.1%
345.2926717 1
< 0.1%
188.4729125 1
< 0.1%
153.8115765 2
0.1%
121.7925197 1
< 0.1%
100.7240046 1
< 0.1%
89.74315607 1
< 0.1%

sulfur_dioxide_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct2584
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6538673 × 10-16
Minimum-2.3722411
Maximum2.5432637
Zeros0
Zeros (%)0.0%
Negative1546
Negative (%)45.0%
Memory size27.0 KiB
2023-10-30T12:12:30.985216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.3722411
5-th percentile-1.8207166
Q1-0.57406105
median0.11249685
Q30.69011422
95-th percentile1.3490814
Maximum2.5432637
Range4.9155048
Interquartile range (IQR)1.2641753

Descriptive statistics

Standard deviation0.93433194
Coefficient of variation (CV)-5.6493767 × 1015
Kurtosis-0.39564908
Mean-1.6538673 × 10-16
Median Absolute Deviation (MAD)0.62987542
Skewness-0.43037712
Sum-6.0007554 × 10-13
Variance0.87297618
MonotonicityNot monotonic
2023-10-30T12:12:31.530840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.000133543 9
 
0.3%
-1.87319504 9
 
0.3%
-1.991387132 8
 
0.2%
-1.86444863 7
 
0.2%
-1.890687862 7
 
0.2%
1.242374814 6
 
0.2%
-1.838209397 6
 
0.2%
-1.785730931 6
 
0.2%
0.0736787078 5
 
0.1%
-1.855702219 5
 
0.1%
Other values (2574) 3369
98.0%
ValueCountFrequency (%)
-2.372241053 2
0.1%
-2.363494642 1
 
< 0.1%
-2.354748231 4
0.1%
-2.34600182 4
0.1%
-2.337255409 2
0.1%
-2.328508998 3
0.1%
-2.319762587 3
0.1%
-2.311016176 3
0.1%
-2.302269765 1
 
< 0.1%
-2.293523354 3
0.1%
ValueCountFrequency (%)
2.543263717 1
< 0.1%
2.476095858 1
< 0.1%
2.414393876 1
< 0.1%
2.366288616 1
< 0.1%
2.055791029 2
0.1%
2.030198571 1
< 0.1%
1.950834098 1
< 0.1%
1.854623578 1
< 0.1%
1.845877167 1
< 0.1%
1.842835302 1
< 0.1%

Interactions

2023-10-30T12:12:20.744449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:11:59.425418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:02.077663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:04.454210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:06.826661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:09.146357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:11.408801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:13.927826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:16.238167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:18.489638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:20.987732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:11:59.728301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:02.331352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:04.675559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:07.083857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:09.380020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:11.635768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:14.151059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:16.461017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:18.745733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:21.213916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:11:59.925799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:02.565784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:04.922267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:07.342241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:09.603307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:11.986031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:14.351492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:16.689826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:18.982128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:21.459103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:00.174381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:02.809576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:05.174453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:07.588520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:09.840614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:12.186850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:14.571732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:16.923353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:19.202887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:21.698997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:00.449215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:03.061230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:05.448033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:07.843507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:10.090087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:12.403064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:14.857816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:17.167194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:19.439137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:21.884478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:00.698842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:03.302163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:05.675405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:08.046818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:10.307398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:12.736222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:15.112644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:17.349281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:19.642581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:22.074692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:00.997679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:03.548256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:05.869482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:08.238408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:10.531783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:13.010504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:15.346624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:17.541868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:19.820406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:22.286037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:01.312434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:03.760012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:06.146474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:08.468904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:10.762108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:13.258988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:15.590390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:17.791213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:20.043425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:22.663341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:01.595224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:03.963458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:06.364350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:08.688818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:10.982857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:13.494997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:15.778667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:18.026948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:20.281806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:22.880026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:01.834791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:04.169122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:06.582486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:08.924218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:11.198879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:13.721467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:16.003000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:18.277910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T12:12:20.499397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-30T12:12:31.764249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidpHsulphatesalcoholqualityresidual_sugar_density_meanchlorides_density_ratiosulfur_dioxide_meanwine_type
fixed acidity1.0000.1860.289-0.2490.224-0.110-0.0950.2620.193-0.2690.507
volatile acidity0.1861.000-0.3040.2000.246-0.039-0.2650.1360.270-0.3870.664
citric acid0.289-0.3041.000-0.2950.0450.0120.1180.098-0.1300.1510.440
pH-0.2490.200-0.2951.0000.2430.1150.055-0.0880.140-0.2100.332
sulphates0.2240.2460.0450.2431.0000.0080.0450.1030.178-0.2490.502
alcohol-0.110-0.0390.0120.1150.0081.0000.485-0.6090.187-0.2520.139
quality-0.095-0.2650.1180.0550.0450.4851.000-0.2540.0140.0120.123
residual_sugar_density_mean0.2620.1360.098-0.0880.103-0.609-0.2541.000-0.1830.2360.479
chlorides_density_ratio0.1930.270-0.1300.1400.1780.1870.014-0.1831.000-0.4070.022
sulfur_dioxide_mean-0.269-0.3870.151-0.210-0.249-0.2520.0120.236-0.4071.0000.716
wine_type0.5070.6640.4400.3320.5020.1390.1230.4790.0220.7161.000

Missing values

2023-10-30T12:12:23.223316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-30T12:12:23.799203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidpHsulphatesalcoholqualitywine_typeresidual_sugar_density_meanchlorides_density_ratiosulfur_dioxide_mean
01.9436120.6091320.611565-1.011681-0.0211030.4772176.0white-0.230202-5.3099670.184676
10.4550662.106564-1.4964351.045176-0.101079-1.0713195.0red-0.0712950.899096-0.451677
21.197274-0.3673740.2035650.4842151.6167341.1314357.0red-0.2733001.665372-1.864449
3-0.974228-0.9745430.5435650.2348991.4766581.0519305.0white-0.9086181.1029210.400251
4-0.665612-0.3673740.815565-0.450720-0.344227-1.1702165.0white0.337231-1.0534601.077442
5-1.406649-0.441161-1.0884350.858189-1.279995-0.0427366.0white0.457152-0.1961790.164098
60.279595-1.293484-0.340435-2.008944-0.760640-1.6797137.0white1.810652-0.1165670.191215
70.710765-1.1326621.155565-0.263733-0.3442270.1335616.0white0.128251-0.4329720.654155
82.2224170.2718962.991565-0.0144170.6003891.8202427.0red0.8899350.302724-1.560899
9-1.518788-0.8964690.543565-0.0144172.0946270.9717968.0white-1.1983450.3772610.962490
fixed acidityvolatile aciditycitric acidpHsulphatesalcoholqualitywine_typeresidual_sugar_density_meanchlorides_density_ratiosulfur_dioxide_mean
34270.0083960.998478-0.2724350.1102410.058350-0.1320495.0red0.1202790.912578-1.855702
3428-0.665612-0.5904961.427565-1.198668-0.845668-1.5757445.0white0.319015-2.3399281.698286
3429-1.296170-0.294161-0.4084351.0451760.1372841.8202426.0red-0.913850-0.435127-2.328509
34300.0083962.078270-2.1764350.7958601.0464431.8202427.0red-0.5478610.117651-1.899434
34312.0142970.340317-0.068435-0.2014041.334943-0.4048067.0red0.3230931.607952-0.886748
34321.504829-0.006875-0.000435-0.201404-0.509079-0.9733775.0white-0.268255-1.0689960.166903
3433-1.5187881.848798-1.9724352.7280580.750906-0.3130694.0red-0.3401899.512583-1.881941
3434-0.869931-1.374938-0.3404350.359557-1.1918750.6447726.0white-1.0501490.4589250.565237
34350.008396-0.974543-0.340435-1.136339-1.2799950.0457967.0white0.47284614.490702-0.272341
34362.222417-0.2941610.5435651.6061362.094627-0.6851065.0red0.7788581.009414-1.421687

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidpHsulphatesalcoholqualitywine_typeresidual_sugar_density_meanchlorides_density_ratiosulfur_dioxide_mean# duplicates
02.2224171.9928731.155565-1.8842861.6167340.5613415.0red1.3609270.661973-1.4479262